Cover Image

Semi-supervised Cervical Segmentation on Ultrasound by A Dual Framework for Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Fangyijie, Curran, Kathleen M., Silvestre, Guénolé
Format:	Preprint
Published:	2025
Subjects:	Image and Video Processing Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Entropy-Guided Agreement-Diversity: A Semi-Supervised Active Learning Framework for Fetal Head Segmentation in Ultrasound
by: Wang, Fangyijie, et al.
Published: (2026)

Evaluate Fine-tuning Strategies for Fetal Head Ultrasound Image Segmentation with U-Net
by: Wang, Fangyijie, et al.
Published: (2023)

Segmenting Fetal Head with Efficient Fine-tuning Strategies in Low-resource Settings: an empirical study with U-Net
by: Wang, Fangyijie, et al.
Published: (2024)

MiTU-Net: A fine-tuned U-Net with SegFormer backbone for segmenting pubic symphysis-fetal head
by: Wang, Fangyijie, et al.
Published: (2024)

Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
by: Wang, Fangyijie, et al.
Published: (2025)

Dual Agreement Consistency Learning with Foundation Models for Semi-Supervised Fetal Heart Ultrasound Segmentation and Diagnosis
by: Wang, Fangyijie, et al.
Published: (2026)

Leveraging Information Divergence for Robust Semi-Supervised Fetal Ultrasound Image Segmentation
by: Wang, Fangyijie, et al.
Published: (2025)

Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images
by: Wang, Fangyijie, et al.
Published: (2025)

Lightweight Framework for Automated Kidney Stone Detection using coronal CT images
by: Wang, Fangyijie, et al.
Published: (2023)

Generative Diffusion Model Bootstraps Zero-shot Classification of Fetal Ultrasound Images In Underrepresented African Populations
by: Wang, Fangyijie, et al.
Published: (2024)

PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
by: Bai, Jieyun, et al.
Published: (2024)

A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPD
by: Zhao, Shuiqing, et al.
Published: (2025)

EUIS-Net: A Convolutional Neural Network for Efficient Ultrasound Image Segmentation
by: Iqbal, Shahzaib, et al.
Published: (2024)

DBF-Net: A Dual-Branch Network with Feature Fusion for Ultrasound Image Segmentation
by: Xu, Guoping, et al.
Published: (2024)

SDCL: Students Discrepancy-Informed Correction Learning for Semi-supervised Medical Image Segmentation
by: Song, Bentao, et al.
Published: (2024)

Semi-supervised Segmentation of Histopathology Images with Noise-Aware Topological Consistency
by: Xu, Meilong, et al.
Published: (2023)

Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training
by: Liu, Zihang, et al.
Published: (2022)

Leveraging Fixed and Dynamic Pseudo-labels for Semi-supervised Medical Image Segmentation
by: Kumari, Suruchi, et al.
Published: (2024)

Dual Invariance Self-training for Reliable Semi-supervised Surgical Phase Recognition
by: Nasirihaghighi, Sahar, et al.
Published: (2025)

DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation
by: Liu, Xinyu, et al.
Published: (2024)

Geo-UNet: A Geometrically Constrained Neural Framework for Clinical-Grade Lumen Segmentation in Intravascular Ultrasound
by: Chen, Yiming, et al.
Published: (2024)

Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image Segmentation
by: Chen, Yaxiong, et al.
Published: (2025)

MonoUNet: A Robust Tiny Neural Network for Automated Knee Cartilage Segmentation on Point-of-Care Ultrasound Devices
by: Kimbowa, Alvin, et al.
Published: (2026)

A Spatial-Temporal Progressive Fusion Network for Breast Lesion Segmentation in Ultrasound Videos
by: Tu, Zhengzheng, et al.
Published: (2024)

Pathology-Guided AI System for Accurate Segmentation and Diagnosis of Cervical Spondylosis
by: Zhang, Qi, et al.
Published: (2025)

CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network
by: Tang, Fenghe, et al.
Published: (2022)

Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction
by: Hashmi, Anam, et al.
Published: (2025)

Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approach
by: Hashmi, Anam, et al.
Published: (2024)

Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Image
by: Zhang, Zerui, et al.
Published: (2024)

Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images
by: Tu, Zhengzheng, et al.
Published: (2024)

Semi-weakly-supervised neural network training for medical image registration
by: Li, Yiwen, et al.
Published: (2024)

DINO-Mix: Distilling Foundational Knowledge with Cross-Domain CutMix for Semi-supervised Class-imbalanced Medical Image Segmentation
by: Liu, Xinyu, et al.
Published: (2026)

UltraBoneUDF: Self-supervised Bone Surface Reconstruction from Ultrasound Based on Neural Unsigned Distance Functions
by: Wu, Luohong, et al.
Published: (2025)

Understanding Task Aggregation for Generalizable Ultrasound Foundation Models
by: Wang, Fangyijie, et al.
Published: (2026)

Semise: Semi-supervised learning for severity representation in medical image
by: Tran, Dung T., et al.
Published: (2025)

Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation
by: Chen, Yuanbin, et al.
Published: (2023)

Automated Cervical Os Segmentation for Camera-Guided, Speculum-Free Screening
by: McDonald-Bowyer, Aoife, et al.
Published: (2025)

Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer
by: Shaik, Abdul Samad, et al.
Published: (2025)

Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning
by: Yang, Jin, et al.
Published: (2025)

FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation
by: Bai, Jieyun, et al.
Published: (2026)